fdars_core/
lib.rs

1//! # fdars-core
2//!
3//! Core algorithms for Functional Data Analysis in Rust.
4//!
5//! This crate provides pure Rust implementations of various FDA methods including:
6//! - Functional data operations (mean, derivatives, norms)
7//! - Depth measures (Fraiman-Muniz, modal, band, random projection, etc.)
8//! - Distance metrics (Lp, Hausdorff, DTW, Fourier, etc.)
9//! - Basis representations (B-splines, P-splines, Fourier)
10//! - Clustering (k-means, fuzzy c-means)
11//! - Smoothing (Nadaraya-Watson, local linear/polynomial regression)
12//! - Outlier detection
13//! - Regression (PCA, PLS, ridge)
14//! - Seasonal analysis (period estimation, peak detection, seasonal strength)
15//! - Detrending and decomposition for non-stationary data
16//!
17//! ## Data Layout
18//!
19//! Functional data is represented using the [`FdMatrix`] type, a column-major matrix
20//! wrapping a flat `Vec<f64>` with safe `(i, j)` indexing and dimension tracking:
21//! - For n observations with m evaluation points: `data[(i, j)]` gives observation i at point j
22//! - 2D surfaces (n observations, m1 x m2 grid): stored as n x (m1*m2) matrices
23//! - Zero-copy column access via `data.column(j)`, row gather via `data.row(i)`
24//! - nalgebra interop via `to_dmatrix()` / `from_dmatrix()` for SVD operations
25
26#![allow(clippy::needless_range_loop)]
27#![allow(clippy::too_many_arguments)]
28#![allow(clippy::type_complexity)]
29
30pub mod matrix;
31pub mod parallel;
32
33pub mod alignment;
34pub mod basis;
35pub mod clustering;
36pub mod depth;
37pub mod detrend;
38pub mod fdata;
39pub mod helpers;
40pub mod irreg_fdata;
41pub mod landmark;
42pub mod metric;
43pub mod outliers;
44pub mod regression;
45pub mod seasonal;
46pub mod simulation;
47pub mod smoothing;
48pub mod streaming_depth;
49pub mod tolerance;
50pub mod utility;
51pub mod warping;
52
53// Re-export matrix types
54pub use matrix::{FdCurveSet, FdMatrix};
55
56// Re-export alignment types and functions
57pub use alignment::{
58    align_to_target, alignment_quality, amplitude_distance, amplitude_self_distance_matrix,
59    compose_warps, elastic_align_pair, elastic_align_pair_constrained, elastic_align_pair_nd,
60    elastic_align_pair_with_landmarks, elastic_cross_distance_matrix, elastic_decomposition,
61    elastic_distance, elastic_distance_nd, elastic_self_distance_matrix, karcher_mean,
62    pairwise_consistency, phase_distance_pair, phase_self_distance_matrix, reparameterize_curve,
63    srsf_inverse, srsf_inverse_nd, srsf_transform, srsf_transform_nd, tsrvf_from_alignment,
64    tsrvf_from_alignment_with_method, tsrvf_inverse, tsrvf_transform, tsrvf_transform_with_method,
65    warp_complexity, warp_smoothness, AlignmentQuality, AlignmentResult, AlignmentResultNd,
66    AlignmentSetResult, ConstrainedAlignmentResult, DecompositionResult, KarcherMeanResult,
67    TransportMethod, TsrvfResult,
68};
69
70// Re-export commonly used items
71pub use helpers::{
72    cumulative_trapz, extract_curves, gradient_uniform, l2_distance, linear_interp,
73    simpsons_weights, simpsons_weights_2d, trapz, DEFAULT_CONVERGENCE_TOL, NUMERICAL_EPS,
74};
75
76// Re-export warping utilities
77pub use warping::{
78    exp_map_sphere, gam_to_psi, inner_product_l2, inv_exp_map_sphere, invert_gamma, l2_norm_l2,
79    normalize_warp, phase_distance, psi_to_gam,
80};
81
82// Re-export seasonal analysis types
83pub use seasonal::{
84    autoperiod, autoperiod_fdata, cfd_autoperiod, cfd_autoperiod_fdata, hilbert_transform, sazed,
85    sazed_fdata, AutoperiodCandidate, AutoperiodResult, CfdAutoperiodResult, ChangeDetectionResult,
86    ChangePoint, ChangeType, DetectedPeriod, InstantaneousPeriod, Peak, PeakDetectionResult,
87    PeriodEstimate, SazedComponents, SazedResult, StrengthMethod,
88};
89
90// Re-export landmark registration types
91pub use landmark::{
92    detect_and_register, detect_landmarks, landmark_register, Landmark, LandmarkKind,
93    LandmarkResult,
94};
95
96// Re-export detrending types
97pub use detrend::{DecomposeResult, TrendResult};
98
99// Re-export simulation types
100pub use simulation::{EFunType, EValType};
101
102// Re-export irregular fdata types
103pub use irreg_fdata::{IrregFdata, KernelType};
104
105// Re-export tolerance band types
106pub use tolerance::{
107    conformal_prediction_band, elastic_tolerance_band, equivalence_test,
108    equivalence_test_one_sample, exponential_family_tolerance_band, fpca_tolerance_band,
109    scb_mean_degras, BandType, EquivalenceBootstrap, EquivalenceTestResult, ExponentialFamily,
110    MultiplierDistribution, NonConformityScore, ToleranceBand,
111};
112
113// Re-export streaming depth types
114pub use streaming_depth::{
115    FullReferenceState, RollingReference, SortedReferenceState, StreamingBd, StreamingDepth,
116    StreamingFraimanMuniz, StreamingMbd,
117};
fdars_core/lib.rs

fdars_core/
lib.rs
